Lu, Chow, and Zhang recently released1 an article detailing some statistical adjustments they claim need to be made when a clinical trial protocol is amended. While I have not investigated their method (they seem to revert to my first choice when there is no obvious or straightforward algorithm – the maximum likelihood method), I do appreciate the fact that they have even considered this issue at all. I have been thinking for a while that the way we tinker with clinical trials during their execution (all for good reasons, mind you) ought to be reflected in the analysis. For example, if a sponsor is unhappy with enrollment they will often alter the inclusion/exclusion criteria to speed enrollment. This, as Lu, et al. point out, tends to increase the variance of the treatment effect (and possibly affect the means as well). But rather than assess that impact directly, we end up analyzing a mixture of populations.
This and related papers seem to be rather heavy on the math, but I will be reviewing these ideas more closely over the coming weeks.